{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:16:36Z","timestamp":1777706196974,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62341115"],"award-info":[{"award-number":["62341115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>\n                    Named entity recognition is a highly relevant research topic in the field of natural language processing owing to its widespread application,\n                    <jats:italic>which mainly involves three types of nested entities, discontinuous entities\u00a0and flat named entities. Notably, nested named entities are characterized by one entity containing multiple sub-entities, ambiguous boundary definitions, and flexible structural formats.\u00a0These features give rise to challenges such as semantic ambiguity, slow decoding efficiency, error propagation, and information loss. Therefore, to effectively address these issues and enhance classification performance, it is critical to integrate information sources such as internal markers, neighbouring word pairs, first and last word pairs, labels, and related spans.<\/jats:italic>\n                    This is achieved in the present study via a newly proposed nested named entity recognition model based on triple cross affine attention. The proposed model encodes the input text using the BERT model and Bi-LSTM before extracting relevant features from the input text by applying DCNN. The extracted feature sequences are used as input into the triple cross affine attention module which computes the scores, allowing the model to classify and predict the outputs using the MLP layer. The experimental results demonstrate that the precision, recall, and F1 value metrics of the proposed method outperform other existing benchmark algorithmic models when applied to the ACE2004, ACE2005, and GENIA standard datasets. Additionally, it exhibits superior recognition performance in nested named entity recognition.\n                  <\/jats:p>","DOI":"10.1177\/18758967251353448","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T02:46:46Z","timestamp":1751338006000},"page":"735-750","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Triple Cross Affine Attention for Nested Named Entity Recognition"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8057-4812","authenticated-orcid":false,"given":"Xiaomeng","family":"Lai","sequence":"first","affiliation":[{"name":"College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1586-5123","authenticated-orcid":false,"given":"Jiayin","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5147-5476","authenticated-orcid":false,"given":"Li","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8987-5492","authenticated-orcid":false,"given":"Lin","family":"Yao","sequence":"additional","affiliation":[{"name":"Personnel Office, Guizhou Minzu University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8019-1490","authenticated-orcid":false,"given":"Youjun","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5160-0588","authenticated-orcid":false,"given":"Xia","family":"Gan","sequence":"additional","affiliation":[{"name":"College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9969-7955","authenticated-orcid":false,"given":"Xinfang","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"issue":"0","key":"e_1_3_3_2_1","first-page":"128.0","article-title":"A deep learning framework for unsupervised affine and deformable image registration","volume":"52","author":"Bob D. 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